For Support & CX Managers
Deflection your customers actually thank you for.
The AI agent that admits when it doesn’t know. Every answer points back to a real help-article page, so QA can audit it, your team trusts it, and customers stop getting confidently-wrong responses. Plug into Zendesk or Intercom in a week. Hold CSAT while you cut tier-1 ticket volume in half.
If any of this sounds familiar
You’ve been burned by an AI rollout before.
The bot deflected. Then CSAT tanked.
You rolled out Fin or Ada or someone last year. Deflection rate looked great in the dashboard. CSAT on AI-only conversations came in 10–15 points below human. QA started flagging hallucinated policy answers. The story to the board got harder by the month.
One screenshot of a confidently-wrong answer. One Slack from Legal.
The bot quoted a refund window that doesn't exist. The bot named a discount we don't offer. The bot suggested an integration we sunset two years ago. Now you're explaining to General Counsel why a customer screenshotted your logo above an invented policy.
Your team thinks they're next.
Every AI rollout becomes a recruiting and retention conversation overnight. Your senior agents start polishing résumés. The roll-back-vs-double-down decision goes to a steering committee. Volume keeps climbing while you sort it out.
We built FlowChat for the team that’s been there. The difference is structural — not "we tried harder this time."
What changes structurally
Three things every other chatbot platform skipped.
Every answer is auditable in QA.
Your QA team can hover any sentence in any conversation and see exactly which page on your help center it came from. No more 'where did the bot get that from?' tickets. No more reading entire transcripts to find the one bad sentence.
The bot says “I don’t know” on purpose.
When your docs don't cover the question, the bot doesn't guess. It refuses, in plain English, with the closest related pages on your help center. 71% of customers click one of those links and find the answer themselves — without opening a ticket.
Shadow mode. Then a phased go-live.
Run the bot in shadow mode against your live ticket queue for two weeks. See the resolution rate, the CSAT projection, and the exact tickets it would have handled — without exposing it to a single customer. Roll out to one channel, one segment, or one tier-1 ticket category at a time.
What pilot customers measure
Numbers that actually go in your QBR.
- 41% fewer tier-1 tickets reaching your team median across pilot tenants, weeks 4–12
- +2 pt average CSAT lift on AI-only conversations vs the no-AI baseline; vs other AI rollouts that drop CSAT 10–15 pt
- ≤6 wks typical payback at mid-market support volume based on a $499/mo plan and a 30 hrs/mo time saving
The ROI calculator on /pricing runs the same conservative deflection ceiling we hold ourselves to on these — so the savings you see are what we’d sign up to deliver.
It plugs into what you already run
No rip-and-replace. Live in front of your existing helpdesk.
The questions you’re actually going to ask
Honest answers to the things that killed your last AI rollout.
What happens when the bot is wrong?
The bot won't write an answer it can't cite. If your docs don't cover the question, it refuses with the closest related pages — visibly, in a different colour from a normal answer. If the bot does write an answer, every sentence links to the page it came from, so QA can spot any drift fast and roll back. We've never had a confidently-wrong answer escape to a customer in pilot.
Will my agents think we're replacing them?
We've watched this play out three times now. The bot handles tier-1 — refunds, password resets, where-to-find-X — and your senior agents get tier-2 and complex tickets they actually wanted to work on. Every team that's rolled this out has kept their full headcount; usually they redirect the saved hours into proactive work like docs improvements and customer onboarding.
How fast is implementation? My last AI rollout took 6 months.
First crawl is ≤60 minutes from sign-up. Shadow mode against your live queue is configurable in a day. Phased go-live (one channel or one ticket category at a time) typically ships in week 2. The 6-month rollouts you're remembering were custom RAG builds — we ship as a product, so the long tail is integration testing with your specific helpdesk, not infrastructure work.
What if our docs are out of date or contradict each other?
We surface this in your admin panel: every refusal generates a 'docs gap' report (“customers asked X 47 times this week — your docs don't cover it”), and contradictions get flagged when two pages give different answers. It's the side benefit nobody warned you about — the bot becomes a docs-quality dashboard.
How does pricing scale? I can't get burned by a viral support week.
Plans are priced on monthly answered questions, with overage at $0.005/answer. A 5x traffic spike turns into a $250 overage line, not a contract renegotiation. The ROI calculator on /pricing shows the math against your real volume.
Will it work with our Zendesk macros and SLAs?
Yes. The bot lives in front of your Zendesk queue: it handles tier-1, leaves your macros and SLA timers untouched, and hands off cleanly to your team for anything it can't answer (with full conversation context attached). We don't replace any of your existing infrastructure — we just shrink the queue.
Ship it the way you’d ship anything else.
Start with a 14-day trial against a single source on your help center. Run shadow mode for a week if you want. Phased go-live to one channel or one ticket category. If the numbers don’t hold up, you cancel — no contract gymnastics. From $199/mo Growth to custom Mid-market.